Systemati.co

The power of data science lies in joining up disparate datasets and extracting insight at the nexus. Effectively integrating various sources of data remains a challenge. It is crucial to understand the nuances between various databases, file formats, enrichment data from third party APIs and the available and relavant metadata. We are experienced with the nuances in working with modern and legacy data sources and can help you shape your data platform to fit the needs of a cutting-edge tech team.

Get in touch right away if you are:

• Looking for the right data format for store your dat in. We can help you navigate between the pros and cons of CSV vs. JSON Lines vs Apache Parquet vs. Apache Arrow. We tailor the system to your needs and access patterns.
• Looking to make your data available across your team, for example by hosting it in a cloud block storage (e.g. AWS S3)
• Tackling the challenging task of joining up multiple data sources and need to address complex issues such as entity resolution and deduplication at scale
• Looking to model your data to fit your needs as a business

The basis for a successful data science program is a well engineered data pipeline. Developing the business logic for your data program is one thing, but setting up a robust automated data pipeline is another thing. Many data scientists are skilled at the investigative part and generate valuable insight, but challenged with establishing a robust pipeline that runs continuously.

Get in touch right away if you are considering:

• building out robust data pipeling infrastructure that allows for a continuously running data program and frees your data science team to focus analysis and keep delivering insights (e.g. with Apache Airflow)
• increasing the level of automation in your data pipeline

You invest a lot in integrating many data sources and analytical processing of your data. Its crucial to make the fruits of your labour readibly available. The tried and tested approch by the most successful companies is to "API everything". Starting with the most granular level, every stage of your data pipeline should be accessible and navigable. To this end, there are multiple API philosophies available and choosing the right thing for the job is important.

Get in touch if you are considering:

• developing APIs for your data (REST, GraphQL)

Making sense of data is an iterative process. We can help you dive into your data and establish a path to a successful data science operation. Through a combination of statistical analysis, dimensionality reductiona and clustering, we will help you understand your data and set the groudwork for robust data models and to building an analysis pyramid.

Get in touch if you would like to:

• perform topological data analysis to uncover hidden connections and patterns that have eluded your statistical analysis to this point
• apply unsupervised machine learning techniques such as dimensionality reduction to explore the underlying features in your data.

A crucial part of data science is to make meaning and match business logic with the technical specification of your data. Data is increasingly connected and often needs to be reshaped into different forms depending on the analysis algorithm. At the heart of a high-performing data science program is a robust data model that meets these requirements and captures the nature of the business.

Get in touch right away if you are:

• in the process of developing a data model as a foundation for a data science program that grows in sophistication.
• embarking on the next iteration of your data schema and your main concern is to make it future-proof.